Decentralized Learning With Limited Communications for Multi-robot Coverage of Unknown Spatial Fields

This paper presents an algorithm for a team of mobile robots to simultaneously learn a spatial field over a domain and spatially distribute themselves to optimally cover it. Drawing from previous approaches that estimate the spatial field through a centralized Gaussian process, this work leverages the spatial structure of the coverage problem and presents a decentralized strategy where samples are aggregated locally by establishing communications through the boundaries of a Voronoi partition. We present an algorithm whereby each robot runs a local Gaussian process calculated from its own measurements and those provided by its Voronoi neighbors, which are incorporated into the individual robot's Gaussian process only if they provide sufficiently novel information. The performance of the algorithm is evaluated in simulation and compared with centralized approaches.Comment: Accepted IROS 202

Nonuniform Coverage Control on the Line

This paper investigates control laws allowing mobile, autonomous agents to optimally position themselves on the line for distributed sensing in a nonuniform field. We show that a simple static control law, based only on local measurements of the field by each agent, drives the agents close to the optimal positions after the agents execute in parallel a number of sensing/movement/computation rounds that is essentially quadratic in the number of agents. Further, we exhibit a dynamic control law which, under slightly stronger assumptions on the capabilities and knowledge of each agent, drives the agents close to the optimal positions after the agents execute in parallel a number of sensing/communication/computation/movement rounds that is essentially linear in the number of agents. Crucially, both algorithms are fully distributed and robust to unpredictable loss and addition of agents

The use of cartograms for BGS data and information representation

Cartograms are shown to be superior to both choropleth maps and bar charts, as they are designed to deliver geospatial statistics effectively and efficiently. This report presents the results of research in establishing the potential for the use of area cartograms for science information delivery. The research involved establishing an overview of all the types of cartogram used; noting the types of data used in their creation and identifying similar sources of data that may prove appropriate for use in cartograms. Test examples were created and presented along with an evaluation as to their effectiveness and efficiency in communicating spatially orientated datasets. The work was carried out as part of the Data Representation and Presentation project for the Geospatial Capture and Solutions team. This forms part of a wider research project investigating new ways in which BGS can display science and information more effectively to a broad range of audiences

Analysis of multi-agent systems under varying degrees of trust, cooperation, and competition

Multi-agent systems rely heavily on coordination and cooperation to achieve a variety of tasks. It is often assumed that these agents will be fully cooperative, or have reliable and equal performance among group members. Instead, we consider cooperation as a spectrum of possible interactions, ranging from performance variations within the group to adversarial agents. This thesis examines several scenarios where cooperation and performance are not guaranteed. Potential applications include sensor coverage, emergency response, wildlife management, tracking, and surveillance. We use geometric methods, such as Voronoi tessellations, for design insight and Lyapunov-based stability theory to analyze our proposed controllers. Performance is verified through simulations and experiments on a variety of ground and aerial robotic platforms. First, we consider the problem of Voronoi-based coverage control, where a group of robots must spread out over an environment to provide coverage. Our approach adapts online to sensing and actuation performance variations with the group. The robots have no prior knowledge of their relative performance, and in a distributed fashion, compensate by assigning weaker robots a smaller portion of the environment. Next, we consider the problem of multi-agent herding, akin to shepherding. Here, a group of dog-like robots must drive a herd of non-cooperative sheep-like agents around the environment. Our key insight in designing the control laws for the herders is to enforce geometrical relationships that allow for the combined system dynamics to reduce to a single nonholonomic vehicle. We also investigate the cooperative pursuit of an evader by a group of quadrotors in an environment with no-fly zones. While the pursuers cannot enter the no-fly zones, the evader moves freely through the zones to avoid capture. Using tools for Voronoi-based coverage control, we provide an algorithm to distribute the pursuers around the zone's boundary and minimize capture time once the evader emerges. Finally, we present an algorithm for the guaranteed capture of multiple evaders by one or more pursuers in a bounded, convex environment. The pursuers utilize properties of the evader's Voronoi cell to choose a control strategy that minimizes the safe-reachable area of the evader, which in turn leads to the evader's capture

Efficient Information Aggregation Strategies for Distributed Control and Signal Processing

This thesis is concerned with distributed control and coordination of networks consisting of multiple, potentially mobile, agents. This is motivated mainly by the emergence of large scale networks characterized by the lack of centralized access to information and time-varying connectivity. Control and optimization algorithms deployed in such networks should be completely distributed, relying only on local observations and information, and robust against unexpected changes in topology such as link failures. We will describe protocols to solve certain control and signal processing problems in this setting. We will demonstrate that a key challenge for such systems is the problem of computing averages in a decentralized way. Namely, we will show that a number of distributed control and signal processing problems can be solved straightforwardly if solutions to the averaging problem are available. The rest of the thesis will be concerned with algorithms for the averaging problem and its generalizations. We will (i) derive the fastest known averaging algorithms in a variety of settings and subject to a variety of communication and storage constraints (ii) prove a lower bound identifying a fundamental barrier for averaging algorithms (iii) propose a new model for distributed function computation which reflects the constraints facing many large-scale networks, and nearly characterize the general class of functions which can be computed in this model.Comment: Ph.D. thesis, Department of Electrical Engineering and Computer Science, MIT, September 201

Path planning, flow estimation, and dynamic control for underwater vehicles

Underwater vehicles such as robotic fish and long-endurance ocean-sampling platforms operate in challenging fluid environments. This dissertation incorporates models of the fluid environment in the vehicles' guidance, navigation, and control strategies while addressing uncertainties associated with estimates of the environment's state. Coherent flow structures may be on the same spatial scale as the vehicle or substantially larger than the vehicle. This dissertation argues that estimation and control tasks across widely varying spatial scales, from vehicle-scale to long-range, may be addressed using common tools of empirical observability analysis, nonlinear/non-Gaussian estimation, and output-feedback control. As an application in vehicle-scale flow estimation and control, this dissertation details the design, fabrication, and testing of a robotic fish with an artificial lateral-line inspired by the lateral-line flow-sensing organ present in fish. The robotic fish is capable of estimating the flow speed and relative angle of the oncoming flow. Using symmetric and asymmetric sensor configurations, the robot achieves the primitive fish behavior called rheotaxis, which describes a fish's tendency to orient upstream. For long-range flow estimation and control, path planning may be accomplished using observability-based path planning, which evaluates a finite set of candidate control inputs using a measure related to flow-field observability and selects an optimizer over the set. To incorporate prior information, this dissertation derives an augmented observability Gramian using an optimal estimation strategy known as Incremental 4D-Var. Examination of the minimum eigenvalue of an empirical version of this Gramian yields a novel measure for path planning, called the empirical augmented unobservability index. Numerical experiments show that this measure correctly selects the most informative paths given the prior information. As an application in long-range flow estimation and control, this dissertation considers estimation of an idealized pair of ocean eddies by an adaptive Lagrangian sensor (i.e., a platform that uses its position data as measurements of the fluid transport, after accounting for its own control action). The adaptive sampling is accomplished using the empirical augmented unobservability index, which is extended to non-Gaussian posterior densities using an approximate expected-cost calculation. Output feedback recursively improves estimates of the vehicle position and flow-field states

EFFICIENT PARAMETRIC AND NON-PARAMETRICLOCALIZATION AND MAPPING IN ROBOTIC NETWORKS

Since the eighties localization and mapping problems have attracted the efforts of robotics researchers. However in the last decade, thanks to the increasing capabilities of the new electronic devices, many new related challenges have been posed, such as swarm robotics, aerial vehicles, autonomous cars and robotics networks. Efficiency, robustness and scalability play a key role in these scenarios. Efficiency is intended as an ability for an application to minimize the resources usage, in particular CPU time and memory space. In the aforementioned applications an underlying communication network is required so, for robustness we mean asynchronous algorithms resilient to delays and packet-losses. Finally scalability is the ability of an application to continue functioning without any dramatic performance degradation even if the number of devices involved keep increasing. In this thesis the interest is focused on parametric and non-parametric estimation algorithms ap- plied to localization and mapping in robotics. The main contribution can be summarized in the following four arguments: (i) Consensus-based localization We address the problem of optimal estimating the position of each agent in a network from relative noisy vectorial distances with its neighbors by means of only local communication and bounded complexity, independent of network size and topology. In particular we propose a consensus-based algorithm with the use of local memory variables which allows asynchronous implementation, has guaranteed exponential convergence to the optimal solution under simple deterministic and randomized communication protocols, and requires minimal packet transmission. In the randomized scenario, we then study the rate of convergence in expectation of the estimation error and we argue that it can be used to obtain upper and lower bound for the rate of converge in mean square. In particular, we show that for regular graphs, such as Cayley, Ramanujan, and complete graphs, the convergence rate in expectation has the same asymptotic degradation of memoryless asynchronous consensus algorithms in terms of network size. In addition, we show that the asynchronous implementation is also robust to delays and communication failures. We finally complement the analytical results with some numerical simulations, comparing the proposed strategy with other algorithms which have been recently proposed in the literature. (ii) Aerial Vehicles distributed localization: We study the problem of distributed multi- agent localization in presence of heterogeneous measurements and wireless communication. The proposed algorithm integrates low precision global sensors, like GPS and compasses, with more precise relative position (i.e., range plus bearing) sensors. Global sensors are used to reconstruct the absolute position and orientation, while relative sensors are used to retrieve the shape of the formation. A fast distributed and asynchronous linear least-squares algorithm is proposed to solve an approximated version of the non-linear Maximum Likelihood problem. The algorithm is provably shown to be robust to communication losses and random delays. The use of ACK-less broadcast-based communication protocols ensures an efficient and easy implementation in real world scenarios. If the relative measurement errors are sufficiently small, we show that the algorithm attains a solution which is very close to the maximum likelihood solution. The theoretical findings and the algorithm performances are extensively tested by means of Monte-Carlo simulations. (iii) Estimation and Coverage: We address the problem of optimal coverage of a region via multiple robots when the sensory field used to approximate the density of event appearance is not known in advance. We address this problem in the context of a client-server architecture in which the mobile robots can communicate with a base station via a possibly unreliable wireless network subject to packet losses. Based on Gaussian regression which allows to estimate the true sensory field with any arbitrary accuracy, we propose a randomised strategy in which the robots and the base station simultaneously estimate the true sensory distribution by collecting measurements and compute the corresponding optimal Voronoi partitions. This strategy is designed to promote exploration at the beginning and then smoothly transition to station the robots at the centroid of the estimated optimal Voronoi partitions. Under mild assumptions on the transmission failure probability, we prove that the proposed strategy guarantees the convergence of the estimated sensory field to the true field and that the corresponding Voronoi partitions asymptotically becomes arbitrarily close to an optimal Voronoi partition. Additionally, we also provide numerically efficient approximation that trade-off accuracy of the estimated map for reduced memory and CPU complexity. Finally, we provide a set of extensive simulations which confirm the effectiveness of the proposed approach. (iv) Non-parametric estimation of spatio-temporal fields: We address the problem of efficiently and optimally estimating an unknown time-varying function through the collection of noisy measurements. We cast our problem in the framework of non-parametric estimation and we assume that the unknown function is generated by a Gaussian process with a known covariance. Under mild assumptions on the kernel function, we propose a solution which links the standard Gaussian regression to the Kalman filtering thanks to the exploitation of a grid where measurements collection and estimation take place. This work show an efficient in time and space method to estimate time-varying function, which combine the advantages of the Gaussian regression, e.g. model-less, and of the Kalman filter, e.g. efficiency

Nonuniform coverage and cartograms

Abstract — In this paper, we summarize our investigation of nonuniform coverage of a planar region by a network of autonomous, mobile agents. We derive centralized nonuniform coverage control laws from uniform coverage algorithms using cartograms, transformations that map nonuniform metrics to a near Euclidean metric. We also investigate time-varying coverage metrics and the design of control algorithms to cover regions with slowly varying, nonuniform metrics. Our results are applicable to the design of mobile sensor networks, notably when the coverage metric varies as data is collected such as in the case of an information metric. The results apply also to the study of animal groups foraging for food that is nonuniformly distributed and possibly changing. I